计算机应用研究2024，Vol.41Issue(10)：2947-2954,8.DOI:10.19734/j.issn.1001-3695.2024.02.0047
利用集成剪枝和多目标优化算法的随机森林可解释增强模型
Interpretability enhancement model of random forest using ensemble pruning and multi-objective evolutionary algorithm
摘要
Abstract
Random forest is a classic black-box model that is widely used in various fields.The structural characteristics of black-box models lead to weak model interpretability,which can be optimized with the help of interpretable techniques to pro-mote the application and development of random forest in scenarios with high reliability requirements.This paper constructed a rule extraction model based on ensemble pruning and multi-objective evolutionary algorithm.Ensemble pruning is an effective method for solving the problem of extracting rules from tree models that tend to fall into local optima,and multi-objective evo-lutionary has several applications in balancing rule accuracy and interpretability.This paper found that it improved interpreta-bility without sacrificing accuracy.This study integrated ensemble pruning technique with a multi-objective evolutionary algo-rithm,which enhances the interpretability of random forests and helps promote the decision-making application of this model in areas with high interpretability requirements.关键词
随机森林/可解释增强/集成剪枝/规则提取/多目标优化算法Key words
random forest/interpretability enhancement/ensemble pruning/rule extraction/multi-objective evolutionary algorithm分类
信息技术与安全科学引用本文复制引用
李扬,廖梦洁,张健..利用集成剪枝和多目标优化算法的随机森林可解释增强模型[J].计算机应用研究,2024,41(10):2947-2954,8.基金项目
国家重点研发计划课题(2021YFC3340501) （2021YFC3340501）
